Università Roma Tre

Dynamical models of stationary processes and prediction
- Physical laws in engineering and science
- Stochastic processes
- Models for filtering, prediction and control: Input-output models for time series and dynamical systems (AR, ARMA, ARX, ARMAX)

Identification
- Black-box identification (Least Squares and Maximum likelihood methods)
- Model complexity selection
- Cross-validation, FPE (Final Prediction Error), AIC (Akaike Information Criterion) or MDL (Minimum Description Length) techniques
- Recursive identification methods (RLS,ELS,RML). Adaptation via forgetting factor techniques

Bayesian filtering
- The state estimation problem. Filtering, prediction and smoothing.
- Kalman filter, steady-state filter Extended Kalman filter
- Unscented transformation, Unscented Kalman filter
- Grid-based filtering
- Particle filtering

Distributed filtering
- Information filter
- Extended Information filter

Sergio Bittanti, "Model Identification and Data Analysis", John Wiley and Sons Ltd